Evolution of Decision Support Systems

Data warehouse  Data warehouse ?  Why Data warehouse ?  What for the Data warehouse ?

The Evolution  1960 (the world of computation consisted of creating individual applications that were run using master files)  1965 (complexity of maintenance and development,synchronization of data, hardware)  1970 (database-a single source of data of all processing)  1975 (online,high performance transaction processing)  1980 (Pcs, 4GL technology)

Problems with the Naturally Evolving Architecture  Data Credibility  No time basis of data  The algorithmic differential of data  The levels of extraction  The problem of external data  No Common source of data from the beginning  Productivity  Inability to transform data into information

The Architected Environment  Level of the architecture  Operational  Detail, day to day, current valued, high probability of access, application oriented  Atomic/data warehouse  Most granular, time variant, integrated, subject oriented, some summary  Departmental  Parochial, some derived; some primitive, typical depts (acct, marketing, engineering, actuarial, manufacturing)  Individual  Temporary, ad hoc, heuristic, non repetitive, pc, workstation based

Who is the user?  What is the different between users of the data warehouse and users of operational environment  Why need DSS analyst ?

The Development Life Cycle  The different between classical SDLC and Data Warehouse SDLC  Classical SDLC  Requirement gathering  Analysis  Design  Programming  Testing  Integration  Implementation  Data warehouse SDLC  Implement warehouse  Integrate data  Test for bias  Program against data  Design DSS system  Analyze results  Understand requirements

Patterns of Hardware Utilization  Major difference between the operational and the data warehouse environments is the pattern of hardware utilization that occurs in each environment.  There are peaks and valleys in operational processing, but ultimately there is a relatively static and predictable pattern of hardware utilization.  There is an essentially different pattern of hardware utilization in the data warehouse environment

Setting the stage for Reengineering  A very beneficial side effect of going from the production environment to the architected, data warehouse environment.

Monitoring the data Warehouse Environment  Two operating components are monitored on a regular basis : the data residing in the data warehouse and usage of the data.  Some of the important results that are achieved by monitoring  Identifying what growth is occurring, where the growth is occcurring and at what rate the growth is occuring  Identifying what data is being used  Calculating what response time the user is getting  Determining who is actually using the data warehouse  Specifying how much of the data warehouse end users are using  Pinpointing when the data warehouse is being used  Recognizing how much of the data ware house is being used  Examining the level of usage of the data warehouse